Abstract
In recent years, the replicability of neuroimaging findings has become an important concern to the research community. Neuroimaging pipelines consist of myriad numerical procedures, which can have a cumulative effect on the accuracy of findings. To address this problem, we propose a method for simulating artificial lesions in the brain in order to estimate the sensitivity and specificity of lesion detection, using different automated corticometry pipelines. We have applied this method to different versions of two widely used neuroimaging pipelines (CIVET and FreeSurfer), in terms of coefficients of variation; sensitivity and specificity of detecting lesions in 4 different regions of interest in the cortex, while introducing variations to the lesion size, the blurring kernel used prior to statistical analyses, and different thickness metrics (in CIVET). These variations are tested in a between-subject design (in two random groups, with and without lesions, using T1-weigted MRIs of 152 individuals from the International Consortium of Brain Mapping (ICBM) dataset) and in a within-subject pre-/post-lesion design [using 21 T1-Weighted MRIs of a single adult individual, scanned in the Infant Brain Imaging Study (IBIS)]. The simulation method is sensitive to partial volume effect and lesion size. Comparisons between pipelines illustrate the ability of this method to uncover differences in sensitivity and specificity of lesion detection. We propose that this method be adopted in the workflow of software development and release.
Highlights
Morphometric neuroimaging pipelines are widely used to study human brain development and diseases
In order to ensure that simulations did not cause unexpected changes in tissue classification outside the selected regions of interest (ROIs), we examined changes in the ratios of gray, white, and CSF tissue classes inside and outside each ROI for Infant Brain Imaging Study (IBIS)-phantom (Figures 4–6)
The changes outside the mask were negligible for all tissue classes and ROIs
Summary
Morphometric neuroimaging pipelines are widely used to study human brain development and diseases. Morphometric pipelines apply several image processing methods to define the shape and boundaries of the white- and gray-matter surfaces, in order to optimally tessellate them, and link the surfaces in biologically plausible ways (Lerch et al, 2005; Tustison et al, 2010; Fischl, 2012; Oguz and Sonka, 2014). As such, these methods are highly sensitive to computational errors arising from parametrization (for example, thresholds of pre-processing parameters that determine the degree of noise reduction or tissue classification)
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